The ubiquitous presence of cell phones in our society is almost universal. It is estimated that there are as many as 6 billion cell phone subscribers on the planet with a population of about 7 billion people at this time. Approximately 1 billion of those cell phones are smartphones, and that number is projected to double by 2015. The technological advances found in smartphones continue to become standard features e.g., cameras, Bluetooth, GPS, touch screens, and WiFi are now commonplace in smartphones. While the functionality of smartphones increase, the costs of purchasing such a mobile device continue to decrease. Similarly, other mobile devices including tablets and laptops have shown ever increasing functionality at lower cost.
Many smartphones are able to obtain location information from, for example, GPS (global positioning satellite) receivers, triangulation based on wireless carrier transmitter locations, or an IP (Internet protocol) address information of a WiFi network to which the smartphone is connected. In addition, many smartphones have internal sensors which determine the orientation of the smartphone, e.g., an accelerometer, a magnetometer, and a gyroscope. Current map programs track a user's location a pre-stored map image and include labels, e.g., for points of interest (POIs), on the large-scale map or on a pre-labeled static image retrieved from a server that may include buildings selected by the user for more information.
However, the conventional map labels do not provide real-time information on the actual surroundings of the user. The user must correlate the downloaded labeled images to his surroundings and visible landscape, which may have changed since the street level image was taken. If a new building was erected or an old building demolished, the user may not recognize his surroundings based on the received static image. If the POIs have changed, the received static image may have updated labels on an outdated image.
As such, there is a need for a system that provides real-time information on visible surroundings to a user by superimposing point of interest (POI) information on the images displayed on the mobile device.